Gentle Ai Bench logo

Gentle Ai Bench

OrganizationPopular
Gentleman-Programming
gentle-ai-bench

Trigger: bench, journey, journeys, driven mode, gentle-ai-bench, journey corpus, j-numbers, bench axis. Author and verify gentle-ai bench journeys; go test ./bench never proves driven execution.

Overview

PublisherGentleman-Programming
Repositorygentle-ai
Skill namegentle-ai-bench
Stars
7K
Forks
760
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Gentleman-Programming on GitHub. Read the source before you install it.

Installation

Install the Gentle Ai Bench AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Gentleman-Programming/gentle-ai.git /tmp/gentle-ai
mkdir -p .claude/skills
cp -r /tmp/gentle-ai/internal/assets/skills/gentle-ai-bench .claude/skills/gentle-ai-bench
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gentle Ai Bench in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gentle Ai Bench on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gentle Ai Bench is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Activation Contract

Load when touching bench/ in gentle-ai, adding or changing a journey, changing a product semantic a journey might pin, or diagnosing a bench failure in CI's Unit Tests job.

Hard Rules

  • go test ./bench validates corpus declarations only. It does NOT execute journeys. The only driven proof is building the harness and the product binary and running the harness against it; a green go test ./bench claims nothing about execution.
  • Reproduce CI, do not guess invocations: read the Unit Tests step in .github/workflows/ci.yml and copy its exact build and gentle-ai-bench run --binary ... commands. Use --only <journey-id> to drive one journey.
  • Journey IDs are unique across every journeys_*.go file. The collision guard fails loudly naming both files; pick an unused ID by reading the corpus, never reuse a retired one.
  • Every journey declares Review: — reviewOptedIn (the runner enables receipt-driven development globally before the first step, uncounted, and fails the journey if the switch does not come on) or reviewUntouched (its subject IS the switch, or it has nothing to do with reviews). The declaration is mandatory; validateCorpus fails the run without it. Never let a journey inherit the product's default: reviews are opt-in, and a journey that assumed otherwise measures a review-refused flow while still reporting completed.
  • Every execute transition must carry a runnable command; the dead-execute guard fails the run otherwise.
  • When a ratified product semantic changes, grep the corpus for journeys pinning the OLD behavior before shipping. The corpus is a second test surface beyond unit tests; a journey asserting the defect keeps the defect green.
  • dead_end prints n/a unless the run actually measured one. Never fabricate a value to move the column.
  • A by_design exemption costs a shape from the closed vocabulary plus a verified quote of the product's own next-action text. If the quote no longer tells the operator what to do, it is a defect wearing an exemption.
  • Prefer a NEW journeys_*.go file when the shared ones are owned by open PRs; bump the core journey-count pin in the same change.

Execution Steps

  1. Read the corpus area you touch and the CI invocation before writing.
  2. Author or adapt the journey; update its title, step names, and comment to say WHY the expectation holds (cite the issue or ratified decision).
  3. Run go test ./... in bench/ for declarations, THEN the driven harness for execution; both results go in the PR body.
  4. On semantic changes, list the journeys you checked for stale pins.

Output Contract

PR evidence includes the driven-mode summary line (completed / unsupported / failed counts) from a locally built binary, not only go test output.

Frequently asked questions

What does the Gentle Ai Bench AI skill do?

Trigger: bench, journey, journeys, driven mode, gentle-ai-bench, journey corpus, j-numbers, bench axis. Author and verify gentle-ai bench journeys; go test ./bench never proves driven execution.

Why use Gentle Ai Bench on TypingMind?

Because you install it once and use it with any model. Gentle Ai Bench is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gentle Ai Bench in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gentleman-Programming/gentle-ai/tree/main/internal/assets/skills/gentle-ai-bench. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gentle Ai Bench?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gentle Ai Bench?

As many as you like. As long as a model supports skills, you can use Gentle Ai Bench with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gentle Ai Bench AI skill free?

Yes. It is published on GitHub by Gentleman-Programming under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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